Image segmentation is typically used to process or modify images to locate objects and boundaries between structures in the images. Thus, segmentation can modify the representation of an image into another representation that is more meaningful and easier to analyze. For example, segmentation of an image can result in the partitioning of the image into multiple regions which can enable the definition of boundaries and the location of objects in images. For digital images having pixels, segmentation can be achieved by similarly characterizing each of the pixels in a region with respect to pixel qualities such as gray levels, colour, texture, etc. Segmentation of sequences of images can also be a useful tool for tracking the position, size and shape of objects as a function of time.
One application of image segmentation is in ultrasound imaging. Ultrasound imaging techniques are commonly used as non-invasive and non-destructive detection and diagnostic tools in a range of industries including medicine, foodstuffs, pharmaceuticals, petrochemicals, chemicals and materials processing. Known techniques take advantage of quantitative ultrasonic parameters such as scattering, backscattering, attenuation, speed of sound, tissue/material nonlinearity and statistics to reveal intrinsic tissue and material properties such as microstructure and composition. The ultrasound image is characterized by pixels of differing intensities that may be used to differentiate different regions of interest. In the case of ultrasound imaging of biological tissues, microstructure and lesions or other abnormalities in the tissue can be detected. Some tissue types can be subjectively identified by their texture. This method has been used successfully to detect or diagnose many medical conditions including atherosclerotic vascular disease, tumors in soft tissue such as the breast and prostate, early Duchenne muscular dystrophy, to monitor cell apoptosis, and to characterize carcinomas, to name a few examples.
Diagnosis from ultrasound images may be hampered by the quality of the images and the nature of the structures being imaged. For example, ultrasound images of soft tissue structures may be low contrast and their often irregularly shaped boundaries further masked by speckle noise, imaging artifacts and shadowing by calcifications in parts of the structures. One such clinical application is in identifying atherosclerotic plaque structures within a vascular wall as the layers of the vascular wall have low contrast and the plaque structures have irregularly shaped boundaries.
Furthermore, the images being analyzed may describe time-varying dynamic structures which can be assessed from a sequence of images in an ultrasound video or a digital Cine loop of an anatomical area. Therefore, for proper diagnosis, a large number of images must be reviewed. If performed manually, this is a time consuming task and subject to variability between observers and subjective interpretation.
Therefore, there is a need for an improved image segmentation method and system.